Presently, there is a revolution with the advent of virtualized computing techniques. Enterprises may allocate computational resources on the cloud, where processing power, storage, and other computational resources are disaggregated from their physical underlying hardware, and abstracted into virtual machines via hypervisors. Virtual machines are isolated from each other and may be allocated and instantiated without impacting the operation of other virtual machines. In effect, clusters of virtual machines can be hosted on one or more computing nodes (i.e., an aggregate of computing resources allocated by a hypervisor).
Virtual machines may act as a multitenant container via software such as Docker. Storage may also be implemented on a non-relational data store platform that scales out thereby becoming sufficiently performant for large amounts of data. In general, parallel processing algorithms, such as map-reduce, have made big data practical, performant, and cost effective. Furthermore, presently there have been recent advances in performant processing, including on large amounts of data to allow for real-time analysis of data or near-real-time analysis of data. One example includes Spark which provides such processing on Hadoop and leverages in-memory computation.
Virtualization creates computing platforms that scale out for large amounts of data. One example of a context that has large amounts of data is with telecommunications. Network protocol analyzers and data sniffers are two types of tools that are often used by network providers such as mobile operators, to analyze the data that is flowing through their systems. Such analysis on the resulting data collection provides valuable insights onto the network, including the quality of the service in one particular area of a network, what services are in use, and whether there are errors and what type of errors are occurring.
However, present techniques to port existing computing infrastructure tend to allocate a virtual machine to correspond to a physical server in an enterprise. For example, an enterprise with a server with a human resources application and a second server with a finance application, might allocate a virtual machine for the human resources application and a virtual machine for the finance application. By deploying legacy apps in this way, potential advantages of the genericity of functionality provided by the virtual machines is lost.
It is with respect to these considerations and others that the present disclosure has been written.